Voice model for speech processing based on ordered average ranks of spectral features

ABSTRACT

Methods and arrangements for generating a voice model in speech processing. Upon accepting at least two input vectors with spectral features, vectors of ranks are created via ranking values of the spectral features of each input vector, ordered vectors are created via arranging the values of each input vector according to rank, and a vector of ordered average values is created via determining the average of corresponding values of the ordered vectors. Thence, a vector of ordered average ranks is created via determining the sum or average of the vectors of ranks, a vector of ordered ranks is created via ranking the values of the ordered average ranks and a spectral feature vector is created via employing the rank order represented by the vector of ordered ranks to reorder the vector of ordered average ranks.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of copending U.S. patentapplication Ser. No. 10/740,661 filed on Dec. 19, 2003, the contents ofwhich are hereby fully incorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to methods and arrangements for generatinga compact model of one or more speakers voice and using the same inspeech synthesis, speech coding, voice transformation, and voice“morphing”.

BACKGROUND OF THE INVENTION

Text-to-speech systems generally include two parts; the first typicallytakes text as input and generates phonetic and prosodic sequences asoutput, and the second, the synthesis step, typically takes the phoneticand prosodic sequences as input and generates audio as output. Severalefforts have historically been made in connection with the second part,but room for improvement continually exists.

Speech synthesis today is mainly done by one of two methods, eitherformant synthesis or concatenative speech synthesis. Formant systems aresmall, but require considerable tuning to achieve acceptable quality,and cannot be automatically matched to a reference voice. Concatenativesystems can be automatically trained to match a reference voice, butmust be quite large to provide acceptable quality, and require a complexdynamic programming process to generate the audio. A need has thereforebeen recognized in connection with providing an arrangement that issmall, fast, and can be easily trained to match a reference voice.

U.S. Pat. No. 5,230,037 (“Phonetic Hidden Markov Model SpeechSynthesizer”; Giustiniani et al.) relates to a system for speechsynthesis that uses sequences of feature vectors chosen from a model setas the basis for synthesizing speech. The feature vectors, however, arecomputed by simple averaging over all instances for each model vector.This has the disadvantage of “smearing” the spectra, resulting indistorted audio upon generation.

Systems for altering voice characteristics, such as U.S. Pat. No.4,624,012 (“Method and Apparatus for Converting Voice Characteristics ofSynthesized Speech”; Lin et al.) and U.S. Pat. No. 5,113,449 (“Methodand Apparatus for Altering Voice Characteristics of Synthesized Speech”;Blanton et al.) rely on modifications of the sampled audio to produce avoice that sounds different, but the types of differences are limited,and they cannot be directed to contain particular desiredcharacteristics. The system for voice transformation discussed in U.S.Pat. No. 5,847,303 (“Voice Processor with Adaptive Configuration byParameter”; Matsumoto) discusses subject matter similar to the Lin etal. and Blanton et al. patents, but uses a set of global parametersestimated from a target speaker to perform the transformation. Similarlyto those patents, however, the changes are not specific to particularsounds, and so are limited.

Some systems for voice transformation use the spectral envelope of thesource speaker together with the excitation signal component of thetarget individual to generate the target signal, for example, U.S. Pat.No. 5,165,008 (“Speech Synthesis Using Perceptual Linear PredictionParameters”; Hermansky et al.) and U.S. Pat. No. 6,336,092 (“TargetedVocal Transformation”; Gibson et al.) which, like Matsumoto, infra,discusses a limited global transformation.

In another system, spectral equalization is performed based on parallelutterances by the source and target speaker (U.S. Pat. No. 5,750,912,“Formant Converting Apparatus Modifying Singing Voice to Emulate ModelVoice”; Matsumoto) but, here, novel utterances are not allowed for.

Other systems use sets of model vectors taken from individual instancesof training data, for example, as discussed in U.S. Pat. No. 5,307,442(“Method and Apparatus for Speaker Individuality Conversion”; Abe etal.), U.S. Pat. No. 5,327,521: “Speech Transformation System”; Savic etal.) and U.S. Pat. No. 6,463,412: “High Performance Voice TransformationApparatus and Method”; Baumgartner et al.). As a result, the modelvectors are subject to noise and variations in the reference speakers'performance, thereby degrading the smoothness of the generated audio.

Some voice coding systems also use model vectors taken from individualinstances of training data, for example U.S. Pat. No. 5,696,879 (“Methodand Apparatus for Improved Voice Transmission”; Cline et al.) and U.S.Pat. No. 5,933,805 (“Retaining Prosody during Speech Analysis for LaterPlayback”; Boss et al.); the same limitations as with Abe et al., Savicet al., and Baumgartner et al. (all supra) are thus apparent.

One method of voice morphing, as discussed in U.S. Pat. No. 5,749,073(“System for Automatically Morphing Audio Information”; Slaney) uses adynamic time warp to align parallel utterances which are interpolatedusing either cross-fading or a dynamic frequency warping. Cross-fading,however, does not blend the voices, but only overlaps them. Dynamicfrequency warping does blend the voices, but the process is complex.

In view of the foregoing, a need has been recognized in connection withimproving upon the shortcomings and disadvantages of prior efforts.

SUMMARY OF THE INVENTION

In summary, one aspect of the invention provides an apparatus comprisinga computer processor configured for executing operations on encodedcomputer program instructions stored in computer memory and arranged forgenerating a spectral feature vector data output, said programinstructions comprising: An apparatus comprising a computer processorconfigured for executing operations on encoded computer programinstructions stored in computer memory and arranged for generating aspectral feature vector data output, said program instructionscomprising: an arrangement for accepting at least two input vectors,each input vector including speech or audio or voice spectral features;an arrangement for creating vectors of ranks via ranking values of thespectral features of each input vector; an arrangement for creatingordered vectors via arranging the values of each input vector accordingto rank; an arrangement for creating a vector of ordered average valuesvia determining the average of corresponding values of the orderedvectors; an arrangement for creating a vector of ordered average ranksvia determining the sum or average of the vectors of ranks; anarrangement for creating a vector of ordered ranks via ranking thevalues of the ordered average ranks; an arrangement for creating aspectral feature vector via employing the rank order represented by thevector of ordered ranks to reorder the vector of ordered average values.

Furthermore, an additional aspect of the invention provides a computerprogram storage device medium readable by a computer processor machine,tangibly embodying an encoded program of instructions executable by themachine to perform method steps for generating a spectral feature vectordata output, said method comprising the steps of: accepting at least twoinput vectors, each input vector including speech or audio or voicespectral features; creating vectors of ranks via ranking values of thespectral features of each input vector; creating ordered vectors viaarranging the values of each input vector according to rank; creating avector of ordered average values via determining the average ofcorresponding values of the ordered vectors; creating a vector ofordered average ranks via determining the sum or average of the vectorsof ranks; creating a vector of ordered ranks via ranking the values ofthe ordered average ranks; creating a spectral feature vector viaemploying the rank order represented by the vector of ordered ranks toreorder the vector of ordered average values.

For a better understanding of the present invention, together with otherand further features and advantages thereof, reference is made to thefollowing description, taken in conjunction with the accompanyingdrawings, and the scope of the invention will be pointed out in theappended claims.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

In accordance with at least one presently preferred embodiment of thepresent invention, a synthesis process can be divided into two parts.The first generates feature vectors from the phonetic and prosodicinput, and the second generates audio from the feature vectors. Thissecond part is accomplished through the method of cepstra regeneration,and in particular uses the “RecoVC” algorithm; this is discussed incopending and commonly assigned U.S. patent application Ser. No.09/432,081, “Method and system for speech reconstruction from speechrecognition features”, filed Nov. 2, 1999, and United States PublishedPatent Application No. 20030088402 (Hoory, Ron et al.; “Method andsystem for low bit rate speech coding with speech recognition featuresand pitch providing reconstruction of the spectral envelope”).

In accordance with at least one presently preferred embodiment of thepresent invention, the first part of the synthesis process is addressed,that is, the generation of the feature vectors. Currently, this is donefor the RecoVC process using a method very similar to concatenativespeech synthesis, as discussed in United States Published PatentApplication No. 20010056347 (Chazan, Dan et al.; Dec. 27, 2001;“Feature-domain concatenative speech synthesis”). This method, however,suffers from many of the drawbacks of the basic concatenative TTSsystems. It uses representations of individual utterances as its basicbuilding blocks, and has a large inventory of these which areconcatenated using a dynamic programming algorithm.

In a probabilistic approach to speech recognition, a finite set ofmodels is defined. Each model is a Markov model, or a probabilisticfinite-state phone machine. This technique is discussed in articles suchas “A Maximum Likelihood Approach to Continuous Speech Recognition”,IEEE Transactions on Pattern Analysis and Machine Intelligence, volumePAMI-5, Number 2, March 1983, by L. R. Bahl, F. Jelinek, and R. L.Mercer.

In accordance with a preferred embodiment of the present invention, onlyone model vector per context dependent phonetic or sub-phonetic state isused, which is trained on multiple utterances of the correspondingsound. This makes the model less susceptible to noise and randomvariations in the speaker's performance. Preferably, the process uses adecision tree as described in U.S. Pat. No. 5,195,167 (“Apparatus andMethod of Grouping Utterances of a Phoneme into Context-DependentCategories Based on Sound-Similarity for Automatic Speech Recognition”;Bahl, Lalit et al) together with the “Forward-Backward Algorithm” fortraining. (See, for example, L. R. Rabiner and B. H. Juang, “AnIntroduction to Hidden Markov Models”, IEEE ASSP Magazine (3) pp. 4-16,January 1986.) The process of generating a sequence of feature vectors,given the prosody and phonetic sequence, is then a simple lookup for themodel vector corresponding to each context dependent sub-phone unit, andapplying smoothing to the transitions, if desired.

In addition to text-to-speech, the presently contemplated process can beused to do voice transformation by extracting the prosody and phoneticsequence from an utterance, and replacing the spectral components whilemaintaining the prosodic information. The result sounds like a differentspeaker saying exactly the same thing in exactly the same tone of voice.Video games or movies may also be able to make use of speech morphing inconjunction with video morphing, beginning either with the prosodygenerated by a text-to-speech process or with the prosody of a recordedutterance.

The model could also be used as a codebook for speech compression. Thesequence of context dependent sub-phone units could be determined byspeech recognition, by standard vector quantization techniques, or by avector quantization technique that is constrained by phonetic context,such as in U.S. Pat. No. 5,455,889 (“Labelling Speech UsingContext-dependent Acoustic Prototypes”; Bahl, Lalit R. et al.)

In accordance with at least one preferred embodiment of the presentinvention, a tremendous difference from prior efforts lies in a methodof accumulating the statistics for the model vectors and a method ofmanipulating the model vectors to generate the sequence of featurevectors from which the audio is generated; key to this is a process thatmay be termed “rank averaging”, discussed herebelow.

Typically, when building models for speech recognition, statistics areaccumulated to be used for computing the means, variances, and priorprobabilities of Gaussian distributions that represent prototypes forfeature vectors belonging to a particular context dependent sub-phoneticphone. If one uses the means of these prototypes to generate the featurevectors for RecoVC, the resulting audio sounds muffled, because theprocess of averaging to generate the mean effectively broadens theformants of the speech spectra. It is not possible to modify the meansto narrow the formants, because the degree of broadening depends on theamount of variation in the position of the formants among the vectors ofthe training data, and this information is not recoverable from theaccumulated statistics.

The spectral features for speech recognition are typically representedas mel frequency cepstra. A Fourier transform is applied to a shortwindowed segment of speech audio to generate a Fourier spectrum. Thespectrum is binned into channels of approximately equal width in termsof mel frequency. The logarithm of each channel is computed, and then acosine transform is used to compute the cepstra. The cepstracoefficients are the values that are accumulated in the training processto generate the Gaussian prototypes.

For rank averaging, however, the cosine transform is preferably notused, but instead the mel log spectra is preferably used directly. In aworking example to now be discussed, 5 mel bins will be used, but 24 isa more typical number of mel bins to be used in actual practice. Foreach feature vector in the training, the Forward-Backward algorithmprovides probabilities that each vector belongs to particular contextdependent sub-phone units. These probabilities are then used as weightsfor accumulating statistics, per the usual practice of applying theaforementioned “Forward-Backward” algorithm to the generation of anacoustic model for speech recognition.

Before accumulating statistics, however, the mel bins are preferablysorted to provide a set of ranks and values. The values are the originalset of 24 values ordered in ascending (or descending) order. The ranksare 5 values, typically 1 to 5, but any linear set of values will do,ordered such that the lowest value is placed in the position of the melbin that had the lowest (or highest) value, the next highest value isplaced in the position of the mel bin with the next higher (or lower)value, and so forth until all 5 values are used. For example, given thehypothetical five dimensional mel log spectrum: [40.6 50.3 55.2 45.746.2], the sorted values would be [40.6 45.7 46.2 50.3 55.2] and theranks would be [1 4 5 2 3].

One then preferably weights the sorted values and the ranks by theprobabilities for each context dependent sub-phone unit and accumulatesthese as two sets of five-dimensional sums. One also preferablyaccumulates the total weight for each context dependent sub-phone unit,as is the usual practice with the aforementioned “Forward-Backward”algorithm.

After all training vectors are processed, one preferably divides thesums for each context dependent sub-phone unit by their correspondingtotal weights. This yields a five-dimensional vector of average valuesand a five dimensional vector of average ranks. Suppose in the presentexample the average values were [41.3 44.2 47.1 52.4 53.9] and theaverage ranks were [1.7 3.6 4.9 2.6 2.2]. One then preferably sorts theaverage ranks and determines their own ranks as was done for thetraining vectors. In this example, the sorted ranks would be [1.7 2.22.6 3.6 4.9] and the ranks of the ranks would be [1 5 4 2 3].

This rank ordering of the average ranks is then assigned to the averagevalues, and the average values are reordered accordingly. In the presentexample, the result would be [41.3 52.4 53.9 47.1 44.2]. This is thusthe rank averaged model vector.

The duration of a context dependent sub-phone unit is typically in therange 1 to 5 frames, where the frames are computed every 10 msec. Ratherthan using the same model vector for all frames of a unit, one can usethe rank averaging process to smooth the transition. For example,suppose one had one unit of duration 2 with model vector [40.0 42.5 46.350.0 43.6] followed by another unit of duration 3 with model vector[41.3 52.4 53.9 47.1 44.2]. Rather than generating the sequence

frame 1 [40.0 42.5 46.3 50.0 43.6]

frame 2 [40.0 42.5 46.3 50.0 43.6]

frame 3 [41.3 52.4 53.9 47.1 44.2]

frame 4 [41.3 52.4 53.9 47.1 44.2]

frame 5 [41.3 52.4 53.9 47.1 44.2]

one could use rank averaging to smooth the vector at frame 3. Here,equal weighting of the two model vectors will be used as an example:frame 2: [40.0 42.5 43.6 46.3 50.0] [1 2 5 3 4]

frame 3: [41.3 44.2 47.1 52.4 53.9] [1 5 4 2 3]

averaged: [40.7 43.4 45.4 49.4 52.0] [1 3 4.5 4 2.5]

ranked: [1 3 5 4 2]

ordered: [40.7 45.4 52.0 49.4 43.4]

to yield:

frame 1 [40.0 42.5 46.3 50.0 43.6]

frame 2 [40.0 42.5 46.3 50.0 43.6]

frame 3 [40.7 45.4 52.0 49.4 43.4]

frame 4 [41.3 52.4 53.9 47.1 44.2]

frame 5 [41.3 52.4 53.9 47.1 44.2]

In the same way that model vectors were averaged across time, one couldalso generate a voice model intermediate between two given models byrank averaging corresponding vectors of each context dependent sub-phoneunit, if both primary models were built using the same set of contextdependent sub-phone units.

If one uses the same pair of weights for every frame, then the resultwill sound like a different speaker. If one gradually changes theweights from 100% for one model to 100% for the other model as theframes progress (i.e., to gradually change the relative weights of themodels from 100%-0% to 0%-100% through various steps such as 90%-10%,80%-20%, 70%-30%, etc.), the result will be a “morphing” (to use avisual analogy) of one voice into the other.

In experimentation, a first model was constructed as the average of 42female speakers and a second model was constructed as the average of 42male speakers. A synthesis blended between these two models, referred tohereabove as “morphing”. Prosody was taken from an utterance by aspeaker not in the training set. The spectral features were generated bythe process described above.

In recapitulation, there is broadly contemplated in accordance with atleast one presently preferred embodiment of the present invention anarrangement for producing a compact voice model from one or morespeakers that includes the process of rank averaging to accumulatestatistics of spectral dimensions of feature vectors, as well as amethod for blending or interpolating those models using rankinterpolation to produce models with desired characteristics (forexample, anonymity in the case of distinct voices associated withindividuals in the entertainment industry, where use of such voicesmight be proprietary or exclusive and where it thus may be desirable toderive a voice belonging to no particular individual). There is alsobroadly contemplated herein: the blending of any other spectral modelswith rank averaging, using a model for speech synthesis, smoothing viarank interpolation across time for synthesis, using a model for speechcoding, using a model for voice transformation, and using two models forvoice morphing.

In further elaboration, morphing may be carried out using rankinterpolation of models that use any other spectral representation.Morphing or blending may be carried out using rank interpolation betweena model and spectral vectors derived for each time frame of real sampledaudio using a speaker independent alignment. (A speaker-dependent modelfor the speaker of the real sampled audio is not necessary in thiscase.) Morphing or blending may be carried out using rank interpolationwithin corresponding phonetic or sub-phonetic units for spectral vectorsderived for each time frame of real sampled audio from parallelutterances using a speaker independent alignments. (Speaker-dependentmodels are also not necessary in this case.)

Feature vectors may be used that contain the ranks and values ofspectral features to make probability distributions to be used as modelsfor speech recognition. Optionally, this could also be done for timederivatives of the spectral representations.

It is to be understood that the present invention, in accordance with atleast one presently preferred embodiment, includes an arrangement foraccepting at least two input vectors, an arrangement for creatingvectors of ranks, an arrangement for creating ordered vectors, anarrangement for creating a vector of ordered average values, anarrangement for creating a vector of ordered average ranks, anarrangement for creating a vector of ordered ranks, and an arrangementfor creating a spectral feature vector. Together, these elements may beimplemented on at least one general-purpose computer running suitablesoftware programs. These may also be implemented on at least oneIntegrated Circuit or part of at least one Integrated Circuit. Thus, itis to be understood that the invention may be implemented in hardware,software, or a combination of both.

If not otherwise stated herein, it is to be assumed that all patents,patent applications, patent publications and other publications(including web-based publications) mentioned and cited herein are herebyfully incorporated by reference herein as if set forth in their entiretyherein.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may beaffected therein by one skilled in the art without departing from thescope or spirit of the invention.

1. An apparatus comprising a computer processor configured for executingoperations on encoded computer program instructions stored in computermemory and arranged for generating a spectral feature vector dataoutput, said program instructions comprising: an arrangement foraccepting at least two input vectors, each input vector including speechor audio or voice spectral features; an arrangement for creating vectorsof ranks via ranking values of the spectral features of each inputvector; an arrangement for creating ordered vectors via arranging thevalues of each input vector according to rank; an arrangement forcreating a vector of ordered average values via determining the averageof corresponding values of the ordered vectors; an arrangement forcreating a vector of ordered average ranks via determining the sum oraverage of the vectors of ranks; an arrangement for creating a vector ofordered ranks via ranking the values of the ordered average ranks; anarrangement for creating a spectral feature vector via employing therank order represented by the vector of ordered ranks to reorder thevector of ordered average values.
 2. The apparatus according to claim 1,further comprising: an arrangement for accepting speech or audio orvoice input; said arrangement for accepting at least two input vectorsbeing adapted to develop input vectors associated with the speech oraudio or voice input spectral features; said apparatus furthercomprising an arrangement for providing probabilities that each inputvector belongs to one or more classes; said arrangements for creating avector of ordered average values and creating a vector of orderedaverage ranks being adapted to assign the probabilities as weights tothe input vectors; and said apparatus further comprising an arrangementfor developing a voice model based on the spectral feature vector. 3.The apparatus according to claim 2, wherein said arrangement forproviding probabilities is adapted to probabilities that each vectorbelongs to particular context-dependent sub-phone units.
 4. Theapparatus according to claim 2, further comprising: an arrangement forgenerating mel frequency log spectra associated with the speech or audioor voice input; said arrangement for generating mel frequency logspectra being adapted to: segment the speech or audio or voice inputinto a plurality of segments; apply a Fourier transform and generating aFourier spectrum for each segment; bin the Fourier spectrum intochannels based on mel frequency; and determine the logarithm of eachchannel.
 5. The apparatus according to claim 4, wherein: saidarrangement for creating vectors of ranks and creating ordered vectorsbeing adapted to sort the mel bins to provide a set of ranks and values;said arrangement for creating a spectral feature vector being furtheradapted to weight the sorted values and ranks with the weights andaccumulate the weighted ranks and weights as two sets of n-dimensionalsums, and develop a total weight for each context-dependent sub-phoneunit; and said arrangements for creating a vector of ordered averagevalues and creating a vector of ordered average ranks being adapted todivide the sums corresponding to each context-dependent sub-phone unitby corresponding total weights to yield a n-dimensional vector ofaverage values and a vector of average ranks.
 6. The apparatus accordingto claim 1, wherein said at least two input vectors correspond to atleast two spectral models, whereby said apparatus is adapted forcreating from at least two spectral models an additional spectral modelhaving predetermined characteristics.
 7. The apparatus according toclaim 2, wherein said at least two input vectors correspond to at leasttwo voice models, whereby said apparatus is adapted for creating from atleast two voice models an additional voice model having predeterminedcharacteristics.
 8. The apparatus according to claim 4, wherein theinput is speech input.
 9. The apparatus according to claim 8, wherein:said arrangement for providing probabilities is adapted to: determinethe probabilities via providing a transcription of the audio input;expand words associated with the transcription into phonetic sequences;and join the phonetic sequences based on a word sequence of thetranscription; determine context-dependent sub-phonetic units for eachphone and employ a speech recognition model to align the result with thesegmented audio input.
 10. The apparatus according to claim 2, whereinthe voice model is used as a vector quantization codebook for speechcoding.
 11. The apparatus according to claim 2, wherein the voice modelis employed in voice transformation via regenerating speech from asequence of model vectors corresponding to the time sequence of classescorresponding to the audio input.
 12. The apparatus according to claim1, wherein said apparatus is adapted for voice morphing via: determininga first time sequence of classes corresponding to one instance of inputspeech; determining a second time sequence of classes corresponding toanother instance of input speech; determining a time-varying weightedaverage of the vectors of corresponding classes; and regenerating speechfrom the resulting sequence.
 13. The apparatus according to claim 2,wherein the voice model is used for voice morphing via: determining atleast one time sequence of classes corresponding to the input speech;determining a time varying weighted average of spectral feature vectorsof: at least two voice models; or at least one input vector and at leastone voice model; and regenerating speech from the resulting sequence.14. The apparatus according to claim 2, wherein the voice model is usedfor speech synthesis via: expanding a sequence of words into phoneticsequences; joining the phonetic sequences based on a word sequence;expanding the phonetic sequence into one or more classes; sequencingvectors associated with the voice model according to a class sequence,and generating speech from the resulting sequence.
 15. The apparatusaccording to claim 1, wherein: the set of input vectors is a contiguousset of vectors taken from a time sequence of spectral feature vectors;and said apparatus further comprises an arrangement for assigningweights for averaging based on the relative position in time of theinput spectral feature vectors.
 16. A computer program storage devicemedium readable by a computer processor machine, tangibly embodying anencoded program of instructions executable by the machine to performmethod steps for generating a spectral feature vector data output, saidmethod comprising the steps of: accepting at least two input vectors,each input vector including speech or audio or voice spectral features;creating vectors of ranks via ranking values of the spectral features ofeach input vector; creating ordered vectors via arranging the values ofeach input vector according to rank; creating a vector of orderedaverage values via determining the average of corresponding values ofthe ordered vectors; creating a vector of ordered average ranks viadetermining the sum or average of the vectors of ranks; creating avector of ordered ranks via ranking the values of the ordered averageranks; creating a spectral feature vector via employing the rank orderrepresented by the vector of ordered ranks to reorder the vector ofordered average values.